| 期刊全稱 | Audio Source Separation | | 影響因子2023 | Shoji Makino | | 視頻video | http://file.papertrans.cn/165/164975/164975.mp4 | | 發(fā)行地址 | Offers the first comprehensive treatment of audio source separation based on non-negative matrix factorization, deep neural network, and sparse component analysis.Describes fundamentals and applicatio | | 學(xué)科分類 | Signals and Communication Technology | | 圖書(shū)封面 |  | | 影響因子 | .This book provides the first comprehensive overview of the fascinating topic of?audio source separation based on non-negative matrix factorization, deep neural networks, and sparse component analysis. .The first section of the book covers single channel source separation based on non-negative matrix factorization (NMF). After an introduction to the technique, two further chapters describe separation of known sources using non-negative spectrogram factorization, and temporal NMF models. In section two, NMF methods are extended to multi-channel source separation. Section three introduces deep neural network (DNN) techniques, with chapters on multichannel and single channel separation, and a further chapter on DNN based mask estimation for monaural speech separation. In section four, sparse component analysis (SCA) is discussed, with chapters on source separation using audio directional statistics modelling, multi-microphone MMSE-based techniques and diffusion map methods.. .The book brings together?leading researchers to provide tutorial-like and in-depth treatments on major audio source separation topics, with the objective of becoming the definitive source for a comprehensive, aut | | Pindex | Book 2018 |
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